The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Curriculum Learning: A Survey
Open accessAbstract
Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs. Curriculum learning strategies have been successfully employed in all areas of machine learning, in a wide range of tasks. However, the necessity of finding a way to rank the samples from easy to hard, as well as the right pacing function for introducing more difficult data can limit the usage of the curriculum approaches. In this survey, we show how these limits have been tackled in the literature, and we present different curriculum learning instantiations for various tasks in machine learning. We construct a multi-perspective taxonomy of curriculum learning approaches by hand, considering various classification criteria. We further build a hierarchical tree of curriculum learning methods using an agglomerative clustering algorithm, linking the discovered clusters with our taxonomy. At the end, we provide some interesting directions for future work.
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Cited by
Agentic Large Language Models, a Survey
Plaat, A., Van Duijn, M., Van Stein, N., Preuss, M., Van der Putten, P., & Batenburg, K. J. (2025) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.18675 · Cited by 75
Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems
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Curriculum learning for language modeling
Campos, D. (2021) · arXiv · doi:10.48550/arxiv.2108.02170 · Cited by 15
(2023) · doi:10.18653/v1/2023.emnlp-main.824 · Cited by 4
CLIMB – Curriculum Learning for Infant-inspired Model Building
Martinez, R. D., McGovern, H., Goriely, Z., Davis, C., Caines, A., Buttery, P., & Beinborn, L. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.10 · Cited by 2
Metadata
- Authors
- Soviany, P., Ionescu, R. T., Rota, P., & Sebe, N.
- Venue
- International Journal of Computer Vision
- Published
- 2022-04-19
- Volume
- 130
- Issue
- 6
- Pages
- 1526-1565
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W3123742938
- Cited by
- 431
- References
- 26
- FWCI
- 26.17
- Type
- journal article
- Language
- en
- Open access
- green
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